A very brief and shallow analysis of the funniest jokes.

The Edinburgh Fringe Festival takes place every year and according to Wikipedia "is the world's largest arts festival, with the 2012 event spanning 25 days totalling over 2,695 shows from 47 countries in 279 venues". Every year at the festival there is a competition for the funniest one line joke. The winner for 2013 was this joke by Rob Auton:

"I hear a rumour that Cadbury is bringing out an oriental chocolate bar. Could be a Chinese Wispa"

You can see all the top 10s over the past year here:

• 2013
• 2012
• 2011
• 2010
• 2009

I've spent an hour or so this evening grabbing all the jokes and doing some very basic analysis of them in Python. The full github repo with all the code (and the data set) can be found here.

Shorter jokes are better

The mean length of the jokes are 88 characters and 17 words. Taking a look at the distribution of the word counts we see that quite a large proportion of the jokes have between 10 and 15 words in them even though there are some jokes that are twice as long.

This does not necessarily imply anything but if we take a look at the rank of a joke against the number of words we see (a very slight) trend towards an affirmation of the fact that shorter jokes rank better:

Joking often does not necessarily help

There are certain comedians who has more than 1 joke in the list of top tens (this does not necessarily imply that they were joking at multiple festivals as some comedians had multiple jokes ni the top 10 of a given year). If we look at the mean and minimum (best) rank per number of jokes:

we see that a part from the 1 comedian who has four jokes there does not necessarily seem to be an advantage of joking often...

The words don't seem too important

One final bit of preanalysis done is to look at the words in each joke. Before removing the common words (I use the nltk python library which has a set of common words) the distribution is given below:

Once we remove the common words we get:

A part from the word 'FAT' which appears in 3 jokes I can't say I recognise any words that I'd normally associate with jokes so I don't suppose there's any particular words worth honing in to.

Conclusion

I can't say that I've found anything too amazing here but if anything the python file in the github repo contains the data from the past 5 years so if I (or anyone else) had time I'd take a closer look at some things...

On a technical note, pretty much everything above was done in pure matplotlib and the nltk library is very easy to use. This is all in the github repo...

An online list of mathematics books

A couple of days ago +James Noble posted a link on G+ to a spreadsheet on G+ that is editable publicly inviting people to contribute mathematics books.

The link to the sheet it here and so far it has 65 books on it (there is also a meta list of lists that has 8 lists of books; to which I've added these 2 previous posts: about mathed and about game theory) and a website I've put together that uses it as an underlying database (which I'll talk about below) can be found here.

I think this is a great idea!

It's completely open and anyone can add to it and also benefit from it. I've used various keywords to search and have found 1 or 2 books that I didn't know about.

Some initial analysis of the list

I decided I'd throw some python at this spreadsheet and first of took a look at the contributors. In particular how many books are individuals contributing (a link to all the python code is available here).

As you can see about 50% of people contribute more than 1 book (including yours truly).

I was also curious as to how many authors were listed:

Here however it seems rare for an author to have more than 1 book in the list. The most prolific author is J. SCOTT CARTER with 5 books.

Some very basic natural language analysis

Two fields in the data set that I have not yet mentioned are the 'Overview' field and the 'Target' field. These two allow for some free text describing what the book is about and who it is aimed at. I used the nltk python library (python really is crazy, anything and everything has a library) to take a look at the frequency of "uncommon" words in these two fields.

First of all the description of the books:

Nothing too surprising here (I should perhaps remove certain words from this frequency analysis) but it's cool to see that "History" is pretty high up, as well as "Teaching" and "Philosophy".

The final piece of analysis is the frequency of the words in the description of who the book is for:

An initial glance at this seems to indicate that the books on the list are mainly aimed at students and or/teachers. It would be nice I suppose to see more books for research...

A very basic static website

The other thing that the code I've put together does is write a website that is hosted using (github pages) and lists the various books (as well as gives up an up to date analysis of what you see above). The website can be found here: 

http://drvinceknight.github.io/An_online_list_of_math_books/

The website is very very basic and all static but I've scripted everything (including the download of the spreadsheet although I haven't included that on github yet) so if it's helpful and anyone wants me to update the site just let me know (give me a nudge on G+ and I even check twitter sometimes).

The github repo for all this is here. If you have any ideas for further stuff that could be done to this data set then please improve my analysis or just let me know :) It would be nice to do something slightly smarter with the nltk and also do some analysis of the links between the books (although it would probably need a bunch more books... for that to be insightful)...

A really great job by +James Noble 

I think stuff like this is really great and it's been nice chatting to James about it.

If you know of any books that aren't on the list yet please do add to the list. 

It was cool to watch the list grow (I saw James's post pretty early so go to watch people make a bunch of contributions) and it'd be nice to see it grow even more.